Abstract
BACKGROUND: The predictive value of (18)F-fluorodeoxyglucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) metabolic parameters for predicting AIP relapse is currently unknown. This study firstly explored the value of (18)F-FDG PET/CT parameters as predictors of type 1 AIP relapse. METHODS: This multicenter retrospective cohort study analyzed 51 patients who received (18)F-FDG PET/CT prior to treatment and did not receive maintenance therapy after remission. The study collected baseline characteristics and clinical data and conducted qualitative and semi-quantitative analysis of pancreatic lesions and extrapancreatic organs. The study used three thresholds to select the boundaries of pancreatic lesions to evaluate metabolic parameters, including the maximum standard uptake value (SUV(max)), mean standard uptake value (SUV(mean)), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and tumor-to-normal liver standard uptake value ratio (SUVR). Univariate and multivariate analyses were performed to identify independent predictors and build a recurrence prediction model. The model was internally validated using the bootstrap method and a nomogram was created for clinical application. RESULTS: In the univariable analysis, the relapsed group showed higher levels of SUV(max) (6.0 ± 1.6 vs. 5.2 ± 1.1; P = 0.047), SUVR (2.3 [2.0-3.0] vs. 2.0 [1.6-2.4]; P = 0.026), and TLG(2.5) (234.5 ± 149.1 vs. 139.6 ± 102.5; P = 0.020) among the (18)F-FDG PET metabolic parameters compared to the non-relapsed group. In the multivariable analysis, serum IgG(4) (OR, 1.001; 95% CI, 1.000-1.002; P = 0.014) and TLG(2.5) (OR, 1.007; 95% CI, 1.002-1.013; P = 0.012) were independent predictors associated with relapse of type 1 AIP. A receiver-operating characteristic curve of the predictive model with these two predictors demonstrated an area under the curve of 0.806. CONCLUSION: (18)F-FDG PET/CT metabolic parameters, particularly TLG(2.5), are potential predictors for relapse in patients with type 1 AIP. A multiparameter model that includes IgG4 and TLG2.5 can enhance the ability to predict AIP relapse.